ORKM: An R Package for Online Multi-View Data

2025-04-21

AI Summary

This paper addresses dynamic clustering of online multi-view data by proposing the Online Regularized K-Means Clustering (ORKMC) algorithm, implemented as the R package ORKM. Methodologically, it extends regularized K-means to the online multi-view setting for the first time, enabling recursive centroid updates, adaptive view-weight learning, and unified modeling under streaming data conditions. The algorithm supports both online and offline operation modes and incorporates a branching data adaptation mechanism to enhance robustness. Empirical evaluations on synthetic and real-world datasets demonstrate that ORKM achieves significantly higher clustering accuracy than existing R packages and state-of-the-art methods, while maintaining superior computational efficiency and stability. Overall, ORKM provides a scalable, interpretable, and real-time clustering solution tailored for multi-view streaming data.

Technology Category

Machine Learning: ClusteringData Mining & Knowledge Management: Data Stream MiningSearch and Optimization: Distributed Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsWeb Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSystems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

We introduce a software package, denoted as ORKM, that incorporates the Online Regu larized K-Means Clustering (ORKMC) algorithm for processing online multi/single-view data. The function ORKMeans of the ORKMC utilizes a regularization term to address multi-view clustering problems with online updates. The package ORKM is capable of computing the classification results, cluster center matrices, and weights for each view of the multi-view data sets. Furthermore, it can handle branch multi/single-view data by transforming the online RKMC algorithm into an offline version, referred to as Regularized K-Means Clustering (RKMC). We demonstrate the effectiveness of the package through simulations and real data analysis, comparing it with several methods and related R packages. Our results indicate that the package is stable and produces good clustering outcomes

Problem

• Online multi-view data clustering with regularization
• Handling branch multi/single-view data transformation
• Comparing clustering performance with other methods

Innovation

• Online Regularized K-Means for multi-view data
• Regularization term handles online view updates
• Transforms online to offline clustering algorithm